News Column

Patent Application Titled "Systems and Methods for Accelerated Dynamic Magnetic Resonance Imaging" Published Online

August 28, 2014



By a News Reporter-Staff News Editor at Computer Weekly News -- According to news reporting originating from Washington, D.C., by VerticalNews journalists, a patent application by the inventors Epstein, Frederick H. (Charlottesville, VA); Chen, Xiao (Charlottesville, VA); Yang, Yang (Charlottesville, VA); Salerno, Michael (Charlottesville, VA), filed on February 6, 2014, was made available online on August 14, 2014.

The assignee for this patent application is University of Virginia Licensing and Ventures Group.

Reporters obtained the following quote from the background information supplied by the inventors: "Dynamic imaging is commonly used in magnetic resonance imaging (MRI), and MRI acceleration techniques can provide enhanced spatial resolution, temporal resolution, and/or spatial coverage for these applications. Compressed sensing (CS), an acceleration technique of growing importance, is making a major impact on MRI (1). Using CS, high-quality images can be recovered from data sampled well below the Nyquist rate, provided that the sampling pattern is incoherent, the images are sparse in a transform domain, and a sparsity-promoting iterative reconstruction is used (1). Because of the high temporal and spatial redundancy inherent to dynamic contrast-enhanced MRI, these data can be represented sparsely in a transform domain and are suited for acceleration by CS (2). However, patient motion due to respiratory or other factors reduces the spatiotemporal redundancy of the data and, if not corrected, leads to image artifacts (3-11). The problem of imperfect breathholding and associated respiratory motion, for example, presents a major challenge to CS-acceleration of first-pass cardiac MRI where, even when patients are instructed to suspend respiration for 15-20 seconds, they are often unable to comply fully with instructions and they breathe during the scan.

"A number of CS methods have been developed to accelerate dynamic MRI. Early studies such as k-t SPARSE showed that sparsity in the spatial and temporal frequency (x-f) domain could be exploited to accelerate cine MRI using CS (12,13). The k-t FOCal Underdetermined System Solver (k-t FOCUSS) method made improvements to x-f domain approaches by separating the data into predicted and residual signals, where the predicted signal served as a baseline signal and sparsity was exploited for the residual signal (4). While x-f domain methods combined with parallel imaging have been successfully used for dynamic contrast-enhanced MRI (3), the non-periodic nature of dynamic contrast-enhanced MRI leads to a broader band of temporal frequencies than cine MRI, thus these applications present less x-f sparsity than cine MRI. For these cases, data-driven spatiotemporal basis functions such as those used in Partially Separable Functions (14) and the k-t Sparsity and Low-Rank (k-t SLR) method (6) may have advantages. For example, the k-t SLR method, which is applied in the image-time domain and exploits matrix rank sparsity by decomposing the signal using singular value decomposition (SVD), has provided good image quality for accelerated contrast-enhanced cardiac perfusion imaging (6). However, even while advanced sparsifying transforms such as SVD provide improved image quality, these approaches are still subject to artifacts when respiratory motion or other patient movement occurs.

"One approach to handle complex dynamics such as breathing is to extract motion information from the acquired data and apply motion compensation during CS reconstruction. Some studies (7,15) base their work on Batchelor's motion matrix method (16) to correct for respiratory motion in free-breathing or real-time cine imaging. While this approach separates cardiac and respiratory motion, the data binning step limits its extension to wider applications such as dynamic perfusion imaging and relaxation imaging. Another approach is to compensate the image dataset for motion and then apply a CS sparsity transform to the motion-compensated data, such as in k-t FOCUSS with motion estimation and compensation (4) and the recent method of Motion-Adaptive Spatio-Temporal Regularization (MASTeR) (17), as well as other methods (3,10,18). To date, these methods have employed the temporal difference or x-f methods as the sparsifying transform, and the results demonstrate advantages afforded by motion compensation.

"It is with respect to these and other considerations that the various embodiments described below are presented."

In addition to obtaining background information on this patent application, VerticalNews editors also obtained the inventors' summary information for this patent application: "The present disclosure relates generally to magnetic resonance imaging (MRI), and more particularly to accelerated dynamic MRI.

"In one aspect, the present disclosure relates to a method for motion compensation and regional sparsity in accelerated MRI. In an example embodiment, the method includes acquiring undersampled MRI data corresponding to a set of consecutive dynamic images associated with an area of interest of a subject, and defining the set of consecutive dynamic images as an initial set of estimated images. The method also includes separating an image of the initial set of estimated images into a plurality of image regions, and performing motion tracking for each image region of the plurality of image regions, throughout the initial set of estimated images, based at least in part on motion data associated with motion of the subject. The method also includes grouping the motion-tracked image regions into a plurality of clusters, based at least in part on spatial contents, and applying a sparsity transform to the plurality of clusters to form a plurality of sparsity-exploited, transformed image regions. The method also includes forming a set of merged images from the plurality of sparsity-exploited, transformed image regions, and updating the set of merged images based on data fidelity, to form an updated set of estimated images.

"In another aspect, the present disclosure relates to a system which, in an example embodiment includes a magnetic resonance imaging (MRI) device, one or more processors, and at least one memory device coupled to the MRI device. The memory device stores computer-readable instructions that, when executed by the one or more processors, cause the system to perform functions that include acquiring undersampled MRI data corresponding to a set of consecutive dynamic images associated with an area of interest of a subject, and defining the set of consecutive dynamic images as an initial set of estimated images. The performed functions also include separating an image of the initial set of estimated images into a plurality of image regions, and performing motion tracking for each image region of the plurality of image regions, throughout the initial set of estimated images, based at least in part on motion data associated with motion of the subject. The performed functions also include grouping the motion-tracked image regions into a plurality of clusters, based at least in part on spatial contents, and applying a sparsity transform to the plurality of clusters to form a plurality of sparsity-exploited, transformed image regions. The performed functions also include forming a set of merged images from the plurality of sparsity-exploited, transformed image regions, and updating the set of merged images based on data fidelity, to form an updated set of estimated images.

"In yet another aspect, the present disclosure relates to a non-transitory computer-readable medium. In one example embodiment, the computer-readable medium has stored computer-executable instructions that, when executed by one or more processors, cause a computer to perform functions that include acquiring undersampled MRI data corresponding to a set of consecutive dynamic images associated with an area of interest of a subject, and defining the set of consecutive dynamic images as an initial set of estimated images. The performed functions also include separating an image of the initial set of estimated images into a plurality of image regions, and performing motion tracking for each image region of the plurality of image regions, throughout the initial set of estimated images, based at least in part on motion data associated with motion of the subject. The performed functions also include grouping the motion-tracked image regions into a plurality of clusters, based at least in part on spatial contents, and applying a sparsity transform to the plurality of clusters to form a plurality of sparsity-exploited, transformed image regions. The performed functions also include forming a set of merged images from the plurality of sparsity-exploited, transformed image regions, and updating the set of merged images based on data fidelity, to form an updated set of estimated images.

"Other aspects and features according to the present disclosure will become apparent to those of ordinary skill in the art, upon reviewing the following detailed description in conjunction with the accompanying figures.

BRIEF DESCRIPTION OF THE DRAWINGS

"FIG. 1 is a diagram illustrating aspects of Block Low-rank Sparsity with Motion-Guidance (BLOSM) in accordance with an example embodiment of the present disclosure. As shown, a set of undersampled dynamic images are divided into blocks (labeled 1 to 5). Motion trajectories for each block are obtained from the current image estimation and utilized to track each block through time. The motion tracked blocks are stacked together to form a cluster for each group of blocks. Each cluster then undergoes a singular value shrinkage step and the resulting blocks are merged into a new estimated image, and the iterations continue for a fixed number of iterations or until a stopping criteria is met.

"FIG. 2 illustrates aspects of tracking blocks of pixels and exploiting regional low-rank sparsity, in accordance with an example embodiment of the present disclosure. As shown, an example block of pixels is tracked throughout the frames. The temporally related blocks with similar spatial contents are gathered together to form a 3D (N.sub.b.times.N.sub.b.times.N.sub.t) cluster. The cluster is rearranged into a 2D matrix (N.sub.s.times.N.sub.t, N.sub.s=N.sub.b.times.N.sub.b), which has a high spatiotemporal correlation. Singular value decomposition (SVD) is applied to the matrix, and only a few of the singular values have significantly higher values than the others.

"FIG. 3 illustrates aspects of block tracking in accordance with an example embodiment of the present disclosure. As shown, m(t.sub.1) and m(t.sub.2) are two consecutive dynamic images. An object (gray circle) is displayed on both images which underwent a translational shift (rightward and upward) from frame to frame. A point is labeled on the circle to represent part of the object. A block B.sub.Xc(t.sub.1) centered at the point is initiated on m(t.sub.1). The point is tracked from m(t.sub.1) to m(t.sub.2), as shown by the arrow. As shown, the tracked point on m(t.sub.2) is not at the center of the pixel. Instead of a spatial interpolation, the pixel containing the dot (the shaded pixel) is selected as the new center pixel for the block. The neighboring pixels are then included to form a tracked block on m(t.sub.2) as B.sub.{Xc+AXc}(t.sub.2), where u.sub.2={u.sub.1+.DELTA.u.sub.1}.

"FIG. 4 illustrates block initialization with and without overlapping blocks, including aspects in accordance with an example embodiment of the present disclosure. In BLOSM, overlapping blocks may be used to avoid gaps. The circles in the figure represent block centers. The solid centers represent initial blocks that cover the whole image, and the unfilled circles represent additional blocks. The initial blocks are demarcated by solid lines, and the additional blocks, which overlap the initial blocks, are demarcated by dashed lines.

"FIG. 5 illustrates reconstruction of retrospectively rate-4 under-sampled data from a computer-simulated phantom that undergo translational shifts, rotation, deformation, through-plane motion, and variable signal intensity over time, and including illustrations of aspects of BLOSM in accordance with example embodiments of the present disclosure. Example reconstructed images at one time frame are shown in the top row. Phantom 1 (P1) undergoes rigid translational shifts along the phase-encoding direction. P2 has an abrupt change in size as well as appearance/disappearance of features to mimic through plane motion combined with translational shifts in the readout direction. P3 undergoes rigid translational shifts along the readout direction. P4 undergoes a gradual change in size which can be interpreted as either cardiac contraction or through-plane motion. P5 rotates counterclockwise through time to mimic object rotation motion. All the phantoms also have quadratically changing signal intensity over time. Corresponding x-t profiles for each phantom (P1-P5) are shown on the bottom panel, where the profile locations are indicated by dashed lines on the fully-sampled image. The first column shows fully-sampled data reconstructed by FFT and serves as a reference. The other columns display undersampled data reconstructed using conventional FFT and the CS methods: BLOSM, k-t FOCUSS with ME/MC, k-t SLR, BLOSM without motion guidance (BLOSM w/o MG) and k-t SLR with global motion guidance (k-t SLR w/ gMG). BLOSM provided the most accurate recovery of the fully sampled images. For k-t FOCUSS with ME/MC, k-t SLR and BLOSM w/o MG, residual artifacts and moderate motion blurring can be observed, especially on P4.

"FIG. 6 illustrates results of implementations of BLOSM, according to example embodiments of the present disclosure, in comparison to other CS algorithms using retrospectively rate-4 undersampled first-pass contrast-enhanced MRI of the heart. Example frames are presented in different rows representing early, mid and late phases of contrast passage. Undersampled data are reconstructed by conventional FFT, BLOSM, k-t FOCUSS with ME/MC, k-t SLR, BLOSM without motion guidance (w/o MG) and k-t SLR with global motion guidance (w/ gMG). Respiratory motion occurred to a large degree during the middle phase (second row) and to a lesser degree at the late phase (third row). BLOSM provided the best image quality for all the phases and very closely matched the fully-sampled images. k-t FOCUSS with ME/MC, k-t SLR and BLOSM w/o MG performed fairly well for phases where there was no or little motion. For the mid phase, severe artifacts can be seen for k-t FOCUSS with ME/MC, k-t SLR and BLOSM w/o MG. k-t SLR w/ gMG resulted in blurred images for all phases. x-t profiles showing similar results are shown on the bottom row, with important dynamic features highlighted by arrows.

"FIG. 7 illustrates a quantitative analysis of the performance of various reconstruction methods applied to rate-4 accelerated first-pass contrast-enhanced MRI of the heart, and including illustrations of aspects of the present disclosure according to example embodiments. Average relative root mean square error (rRMSE) and structural similarity (SSIM), averaged over time, of the CS-reconstructed images were compared to fully-sampled reference images. BLOSM achieved the lowest error (rRMSE) and highest similarity (SSIM) compared to the CS methods. (* P

"FIG. 8 illustrates convergence of BLOSM for various initial block sizes, according to example embodiments of the present disclosure. BLOSM using different initial block sizes was used to reconstruct a first-pass perfusion dataset. These rRMSE vs. iteration curves demonstrate that the convergence of BLOSM is essentially independent of the initial block size (not all tested initial block sizes are shown, but all had similar behavior).

"FIG. 9 illustrates convergence of BLOSM under various conditions, including aspects of in vivo perfusion imaging, in accordance with example embodiments of the present disclosure. Panels (A and B) show for both computer-simulated phantoms and in vivo perfusion imaging that the coarse-to-fine strategy provides lower rRMSE compared to not using this strategy. Similarly, panels (C and D) show that for both computer-simulated phantoms and in vivo perfusion imaging the use of motion guidance reduces rRMSE compared to not using motion guidance.

"FIG. 10 illustrates aspects of BLOSM in accordance with example embodiments of the present disclosure, including images reconstructed using BLOSM and histograms from multiphase difference images. Image reconstruction using BLOSM is not highly dependent on the initial block positions. Images were reconstructed using BLOSM with original initial block positions (A) and with shifted initial block positions (D). The difference between a fully-sampled 2DFT-reconstructed image (Reference image) and (A) is shown in (B), and the difference between the reference image and (D) is shown in (E). Histograms from multiphase difference images using the original initial block positions and the shifted initial block positions are shown in (C) and (F), respectively. Using either the original initial block positions or the shifted initial block positions results in the same difference distribution (Gaussian distribution verified using the Jarque-Beta test) compared to the reference images. .mu. is the mean and .sigma..sup.2 is the variance.

"FIG. 11 is a flow chart illustrating operations of a method in accordance with an example embodiment of the present disclosure.

"FIG. 12 is a system diagram illustrating an operating environment capable of implementing aspects of the present disclosure in accordance with one or more example embodiments.

"FIG. 13 is a computer architecture diagram illustrating computer hardware architecture for a computing system capable of implementing aspects of the present disclosure in accordance with one or more example embodiments."

For more information, see this patent application: Epstein, Frederick H.; Chen, Xiao; Yang, Yang; Salerno, Michael. Systems and Methods for Accelerated Dynamic Magnetic Resonance Imaging. Filed February 6, 2014 and posted August 14, 2014. Patent URL: http://appft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&u=%2Fnetahtml%2FPTO%2Fsearch-adv.html&r=4095&p=82&f=G&l=50&d=PG01&S1=20140807.PD.&OS=PD/20140807&RS=PD/20140807

Keywords for this news article include: Cardiology, University of Virginia Licensing and Ventures Group.

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